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In this paper, the authors analyze the lean production and technological innovation in manufacturing industry based on SVM algorithms and data mining technology. Data mining can discover novel, effective, potential and ultimately understandable data patterns from a deeper level, and encode the data to predict the development trend of enterprises. The machine learning support vector machine method is used to analyze and model the collected data. At the same time, we constructed a decision tree using random forest, and explained the significance of the training algorithm through the visualization results. The simulation results show that learning growth dimension and market dimension have the greatest impact on business model innovation. In the context of TEC, business model innovation must pay attention to market grasp and customer demand oriented, so as to improve the competitiveness of manufacturing enterprises.<\/jats:p>","DOI":"10.3233\/jifs-179217","type":"journal-article","created":{"date-parts":[[2019,6,7]],"date-time":"2019-06-07T11:47:41Z","timestamp":1559908061000},"page":"6377-6388","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":4,"title":["Lean production and technological innovation in manufacturing industry based on SVM algorithms and data mining technology"],"prefix":"10.1177","volume":"37","author":[{"given":"Zhen","family":"Zhen","sequence":"first","affiliation":[{"name":"School of Business, Nanjing Normal University, Nanjing, Jiangsu, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yao","family":"Yanqing","sequence":"additional","affiliation":[{"name":"CDP Group Limited, Shanghai (Global Headquarter), China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2019,6,6]]},"reference":[{"key":"e_1_3_1_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIA.2012.2190816"},{"key":"e_1_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TCYB.2013.2279167"},{"key":"e_1_3_1_4_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10346-013-0391-7"},{"key":"e_1_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1002\/widm.1132"},{"key":"e_1_3_1_6_2","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-012-1324-4"},{"key":"e_1_3_1_7_2","doi-asserted-by":"publisher","DOI":"10.1109\/MWC.2014.6757897"},{"issue":"11","key":"e_1_3_1_8_2","first-page":"1527","article-title":"Failure and reliability prediction by support vector machines regression of time series data","volume":"96","author":"das M.","year":"2017","unstructured":"dasM., ChagasMoura, ZioE. and LinsI.D., et al., Failure and reliability prediction by support vector machines regression of time series data, Reliability Engineering & System Safety 96(11) (2017), 1527\u20131534.","journal-title":"Reliability Engineering & System Safety"},{"issue":"2","key":"e_1_3_1_9_2","article-title":"Application of six sigma methodology to reduce waiting times at okayama university hospital","volume":"2008","author":"Benitez J.","unstructured":"BenitezJ., MiyazakiS., YanagawaY. and OkuboH., Application of six sigma methodology to reduce waiting times at okayama university hospital, Produetion Management 2008(2).","journal-title":"Produetion Management"},{"key":"e_1_3_1_10_2","first-page":"42","article-title":"Performance evaluation of collaborative innovation center in universities of henan province based on two-dimensional matrix","author":"Luo D.","year":"2016","unstructured":"LuoD. and WangJ., Performance evaluation of collaborative innovation center in universities of henan province based on two-dimensional matrix, Journal of North China University of Water Resources & Electric Power (2016), 42\u201350.","journal-title":"Journal of North China University of Water Resources & Electric Power"},{"issue":"3","key":"e_1_3_1_11_2","first-page":"115","article-title":"Pattern of information technology use: The impact on buyer\u2013 suppler coordination and performance","author":"Sanders R. 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